Indiana completed the 2025 season 16-0. It additionally ranked first in common scoring margin, first in Easy Ranking System, and first in Elo. That half is simple.
The extra helpful circumstances are those the place the strategies disagree.
Notre Dame ranked thirteenth in win share however third in each scoring margin and SRS, and second in Elo. Georgia and James Madison each completed 12-2, which tied them for fourth in win share. Georgia ranked tenth in SRS and fifth in Elo. James Madison ranked twenty ninth in SRS and thirteenth in Elo.
Crew
Win % rank
Level differential rank
SRS rank
Elo rank
Indiana
1
1
1
1
Notre Dame
13
3
3
2
Georgia
4
15
10
5
James Madison
4
7
29
13
Which rating is true?
I feel that query is just too broad. Every methodology is answering a distinct query, and each fails otherwise. The higher query is: proper for what objective?
Begin with one clearly outlined season
I used all accomplished video games involving a 2025 FBS staff, together with the postseason and video games in opposition to FCS opponents. Last scores embody extra time. Impartial-site video games depend usually as a result of neither win share nor scoring margin wants a home-field adjustment.
The data endpoint and Video games API reconciled precisely: all 136 FBS groups had the identical sport depend in each sources. That gave me one constant set of outcomes for win share and level differential.
SRS and Elo are somewhat totally different. I pulled the end-of-season values from the CollegeFootballData scores endpoints and filtered them to the identical 136-team FBS inhabitants. These are precomputed scores and the endpoint responses don’t expose each inner selection behind the calculations, so I’d not declare that I rebuilt CFBD’s SRS and Elo from an similar sport set (each notably exclude video games in opposition to FCS opponents).
That distinction issues. A rating is not only a formulation. It’s also a set of selections about which video games depend, the place groups begin, how rapidly scores change, and the way schedule energy is dealt with.
The API requests themselves are brief. This instance assumes you may have already created an authenticated CFBD consumer:
YEAR = 2025
data = games_api.get_records(yr=YEAR)
video games = games_api.get_games(
yr=YEAR,
season_type=cfbd.SeasonType.BOTH,
classification=cfbd.DivisionClassification.FBS,
)
srs = ratings_api.get_srs(yr=YEAR)
elo = ratings_api.get_elo(yr=YEAR)
1. Win share: what did the staff accomplish?
Win share is essentially the most clear measure within the group:
Win share = (wins + 0.5 × ties) ÷ video games performed
There have been no ties within the 2025 FBS information, however together with them makes the definition full since ties traditionally occurred in faculty soccer. The corresponding code stays near that definition:
win_percentage_rows = []
for report in data:
if report.staff not in fbs_team_names:
proceed
games_played = report.complete.video games
wins_with_ties = report.complete.wins + 0.5 * report.complete.ties
win_percentage_rows.append({
“staff”: report.staff,
“win_percentage”: wins_with_ties / games_played,
})
win_percentages = pd.DataFrame(win_percentage_rows)
The enchantment is apparent. Video games are performed to be gained, and a standings or résumé abstract ought to stay related to that final result. Indiana’s 16-0 report belongs on the prime of that form of rating.
The failure mode is simply as apparent: groups don’t play equal schedules, and a one-point win counts precisely as a lot as a 40-point win. Win share can inform us what occurred with out telling us how dominant the staff regarded or how tough the trail was.
Kennesaw State is a helpful instance. It completed 10-4 and ranked twenty third in win share. Seven of its wins got here by 14 factors or fewer, whereas losses to Indiana and Western Michigan got here by 47 and 35 factors. The report correctly credit Kennesaw State for successful 10 video games. It doesn’t seize the form of these outcomes.
2. Level differential: how dominant did the staff look?
Common level differential provides the rating:
Common level differential = complete factors scored minus complete factors allowed, divided by video games performed
I reshape every accomplished sport into one row per FBS staff. A optimistic margin means the staff outscored its opponent:
margin_rows = []
for sport in video games:
if not sport.accomplished:
proceed
if sport.home_points is None or sport.away_points is None:
proceed
if sport.home_team in fbs_team_names:
margin_rows.append({
“staff”: sport.home_team,
“margin”: sport.home_points – sport.away_points,
})
if sport.away_team in fbs_team_names:
margin_rows.append({
“staff”: sport.away_team,
“margin”: sport.away_points – sport.home_points,
})
point_differential = (
pd.DataFrame(margin_rows)
.groupby(“staff”, as_index=False)[“margin”]
.imply()
)
This is the reason Kennesaw State falls from twenty third in win share to 74th in level differential. The strategy sees each the shut wins and the lopsided losses.
Level differential additionally explains why Notre Dame appears to be like stronger than its 10-2 report alone. The 2 losses got here by three factors to Miami and one level to Texas A&M. The Irish then gained 10 straight, together with eight video games by at the least 15 factors, and completed third in common scoring margin.
That added sign comes with a judgment name. Ought to a 50-point win depend way more than a 30-point win? Massive margins can embody details about dominance, however they will additionally mirror rubbish time, late-game incentives, opponent depth, and training choices which have little to do with future staff energy.
I examined caps of 14, 21, and 28 factors per sport. The selection was not beauty. A 21-point cap moved Florida State from twenty sixth to 62nd within the point-differential rating and Coastal Carolina from a hundred and twentieth to 94th. If an affordable parameter modifications the ordering that a lot, the rating needs to be offered as a mannequin output quite than an goal truth.
3. SRS: how robust had been the opponents?
Easy Ranking System begins with scoring margin and adjusts it for schedule energy. In plain language, beating a powerful staff ought to depend for greater than beating a weak staff, and shedding to a powerful staff ought to harm lower than shedding to a weak staff.
The round half is the purpose. We’d like opponent energy to charge a staff’s outcomes, however we additionally want outcomes to estimate opponent energy. SRS resolves that by way of a related schedule community, repeatedly updating groups till the scores settle.
James Madison exhibits what that adjustment can do. The Dukes completed 12-2 and seventh in level differential at +18.7 factors per sport. A lot of the wins got here in opposition to a Solar Belt schedule, whereas the 2 losses had been to Louisville and Oregon. SRS moved James Madison to twenty ninth after accounting for the entire opponent community.
Georgia moved the opposite path relative to uncooked margin. The Bulldogs had been solely fifteenth in level differential, however they ranked tenth in SRS after a schedule that included Tennessee, Alabama twice, Ole Miss twice, Texas, Florida, and Georgia Tech.
SRS is helpful when the query is season-long, schedule-adjusted efficiency. It might probably nonetheless be fragile early in a season, when the schedule community is skinny and lots of groups haven’t performed frequent or related opponents. Outcomes additionally rely on implementation selections: whether or not to incorporate FCS video games, easy methods to deal with margin caps, and whether or not to weight current video games in a different way.
4. Elo: what did we imagine earlier than and after every sport?
Elo works sequentially. Every staff has a pregame score. The distinction between the 2 scores produces an anticipated end result, and the precise end result determines the postgame replace.
4 selections drive a primary implementation:
Preliminary score: the place each staff begins, or how a lot prior-season data carries ahead.Okay-factor: how rapidly one end result can change a score.Residence-field benefit: how a lot the anticipated end result shifts for the house staff.Margin therapy: whether or not a detailed win and a blowout produce the identical replace.
This makes Elo helpful for updating an estimate by way of time and for producing pregame expectations. It additionally makes Elo path dependent. Two groups with the identical closing report can end with totally different scores as a result of they began elsewhere, confronted totally different opponents, and reached these leads to a distinct order.
South Carolina is the sharpest instance on this comparability. The Gamecocks completed 4-8, rating 98th in win share and 76th in level differential, however twenty ninth in CFBD Elo. That doesn’t imply Elo believes 4 wins are higher than 10. It means Elo shouldn’t be a standings desk. Its output displays incremental updates to a previous score and the energy of the groups concerned.
I’d not attribute South Carolina’s precise rating to 1 parameter with out rebuilding CFBD’s full Elo historical past. The endpoint end result remains to be helpful as a result of it exposes the strategy’s central tradeoff: prior data can stabilize a score, however it may additionally maintain the score distant from the current-season report.
To check sensitivity, I additionally replayed the 2025 season with a intentionally easy Elo mannequin that began each staff at 1500. Altering the Okay-factor from 10 to 40 moved Duke 18 rating positions and UNLV 17. Eradicating a 55-point home-field score adjustment modified some groups once more. None of these settings is mechanically appropriate. Every describes how rapidly we would like new proof to interchange the outdated estimate.
Match the strategy to the query
If you wish to measure…
Begin with…
Look ahead to…
Standings or résumé
Win share
Unequal schedules and no margin data
Descriptive dominance
Level differential
Blowouts, rubbish time, and margin-cap selections
Schedule-adjusted season energy
SRS
Early-season connectedness and implementation selections
Updating estimates and forecasting
Elo
Initialization, path dependence, Okay-factor, and residential area
Extra refined doesn’t imply freed from judgment. SRS and Elo can appropriate weaknesses in uncooked standings, however they introduce assumptions of their very own. A helpful score makes these assumptions seen and matches them to the choice the reader is making an attempt to make.
If you wish to reproduce the comparability, the CFBD API documentation covers the video games, data, and scores endpoints used right here. The CFB Starter Pack gives a guided pocket book path for those who would quite start with a structured workflow.
Which methodology do you belief for which objective: win share, level differential, SRS, or Elo? And which assumption would you modify first?
